Lead Analytics Engineer (Looker)

From empowering entrepreneurs to build successful businesses to supporting their clients in arranging "me time" moments, Booksy is an international, cloud-based marketplace scaling rapidly. The company connects independent business owners with their customers, focusing on creative communities and opportunities. Booksy operates in a rapidly growing, ever-changing scale-up environment.

Technical Skills:

• Expert-level knowledge of Looker Semantic Layer, LookML, Liquid, and Looker administration.
• Experience with Looker API and migrating from other tools to Looker is highly advantageous.
• Proficiency in SQL (advanced window functions, optimization) and experience with cloud data warehouses (e.g., Snowflake, BigQuery).
• At least intermediate knowledge of dbt.
• Strong understanding of Git workflows, CI/CD principles, and data modeling methodologies (Kimball, Data Vault, etc.).
• Ability to write Python to automate workflows or interact with APIs.
• Prior experience with AI Agents within conversational analytics is highly desirable.

Leadership & Soft Skills:

• Proven experience leading or mentoring a team of engineers in an agile environment.
• Ability to explain complex technical trade-offs to non-technical stakeholders in Marketing or Finance.
• Passion for documentation, Jira hygiene, and building repeatable processes.

People Leadership & Development:

• Lead the Analytics Engineering team, driving a high-performance culture and supporting individual career growth.
• Conduct regular 1-on-1s, provide constructive feedback, and manage performance reviews.
• Manage team capacity and sprint priorities, balancing technical debt with stakeholder needs.
• Assist with recruitment and ensure smooth onboarding for new joiners.

Semantic Architecture & Governance:

• Lead the design, development, and maintenance of centralized Looker semantic models (LookML).
• Act as the "Gatekeeper" for Looker, enforcing coding standards, modularity, and performance optimization.
• Establish and manage version control and deployment pipelines in GitLab for the semantic layer.

Cross-Functional Collaboration:

• Partner with Data Engineering to define table structures and schemas optimizing downstream analytical performance.
• Translate business needs of Corporate, Marketing, CS, Sales, and GTM Ops Analysts into scalable data models.

Automation & Efficiency:

• Identify manual workflows and automate them using Python scripts, API integrations (Looker API), or Agentic AI.
• Co-create and enforce a disciplined Jira ticketing structure to ensure transparency and velocity.

• Candidates must be based in the country/region where the role is advertised.
• Applications and CVs must be submitted in English.
• Commitment to diversity and inclusion with support for accessibility needs during the interview process.